Traditionally data governance has been around the people and process side of data management. However we now see tools marketed as data governance tools either as a pure play tool for data governance or as a part of a wider data management suite as told in the post Who needs a data governance tool?

The post refers to a report by Sunil Soares. In this report data governance tools are seen as tools related to six areas within enterprise data management: Data discovery, data quality, business glossary, metadata, information policy management and reference data management.

While IBM have tools for everything, according to the report it does not seem like a single tool cures it all – yet.

But will we go there? If we need tools at all, do we need an all-cure snake oil tool for data governance? Or will we be better off with different lubricants for data discovery, data quality, business glossary, metadata, information policy management and reference data management?

People and process remain critical to data governance. The challenge in a Big Data world is that people and process are quickly overwhelmed by the volume and pace of data, as well as the complexity and diversity of tools and algorithms working with the data. Without tools and automation there is very little chance of managing/governing the information.
In regards to number of tools needed, I think it depends on what you can put effectively put together. There are very close links between all of these facets. Data quality, for example, does not exist in isolation. It is based on policy, which is applied to business terms, which is connected to multiple points of enforcement, which are measured by business indicators (not just the number of items which meet or don’t meet your quality rules), which must roll back together to give you some sense of how well you are meeting or complying to your policies, and which have some approach for remediation and resolution.
Yes, you can stitch separate pieces together if there are connections available to do so (most likely the metadata, the master data, and the glossary), but there is a human cost to do that stitching and it often doesn’t scale – a significant factor in dealing with Big Data.

Vendors are keen to sell “one stop shop” tools to fix any DG challenge in any industry, for any client. Like the aforementioned snake oil salesmen of a century ago who sold the same remedy for everything from headaches and heart attacks, one tool will not a data challenge solve.

It will always require thoughtful, careful analysis and strategy development by competent professionals first, before any talk at all of tools enters the discussion.

Vendors (including my own) are keen to sell ANY tools, whether ‘one stop shop’ or ‘best of breed’. And they do so because there are needs for those tools.
As Bill points out, analysis and strategy are critical for an organization undertaking, expanding, or maturing their data governance initiatives, particularly in understanding the driving needs they must address and what capabilities are required to support them.

I’m not sure there will ever be a one-size fits all response to your thought-provoking post Henrik. By posing the questions, you’re at least bringing the issue of DG tools into people’s consciousness so that they can give it some mindful consideration.

For my part, I am of the opinion that software tools can play a part in enabling better co-ordination, communication and collaboration of informational issues within a business. However, this needs to be set in the context of Data Governance being a very human problem.

At present, I don’t see any of the tool vendors addressing this human context at all – they’re all trying to sell their tool as a technological panacea to the Data Governance challenge(s).

For the time being, at least, I’m of the view that buying a tool isn’t the end of your problems, it’s only the beginning…

Thanks for adding in Gary, Harald, Bill and Alan. My guess is that data governance will and should be a people and process thing. We will see tools addressing specific data governance requirements as business glossary and small reference data management. But mainly we will see data governance and data stewardship support built into various wider data management offerings not at least data quality tools and Master Data Management (MDM) solutions.

Pushing a bit further on Alan and Henrik’s comments, I think it is not only a people and process thing, but one that requires a clear information strategy with defined business outcomes. The latter establish a framework within which you can set the coordination and communication requirements that are critical to successful Information Governance.
The emerging challenge that I see amongst organizations, though, is scale. What works amongst smaller groups or with specific subsets of data, breaks down as the volume of data increases. Yet the policies that organizations must implement are not getting simpler. And the number of people available to work on governance is not increasing at the same rate as data volume. This challenge of connecting the policies to be implemented and enforced with the data on which it is to be applied and then monitored and reported in a way that humans can consume and respond to seems, to me, to be the critical issue to address regardless of the number of tools at hand.

Henrik, my experience is completely in line with your observations. So much of data governance is contingent on how an organization is managed. Because of that contingency, the people/process part of the equation will vary widely even for organizations within the same industry. I am skeptical that the technology piece of the equation will be widely applicable.